Identification of Causal Structure in the Presence of Missing Data with Additive Noise Model
加性噪声模型下含缺失数据的因果结构识别AI 解读 ↓
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加性噪声模型下含缺失数据的因果结构识别
- 研究问题
- 缺失概率依赖缺失值本身、导致联合分布无法恢复时，能否仍在加性噪声模型下识别因果骨架与方向，并放宽无自遮蔽缺失假设？
- 方法与思路
- 作者研究不同缺失机制下的加性噪声模型，将骨架识别扩展至弱自遮蔽缺失，给出方向识别的充要条件，并构建相应学习算法。
- 材料陈述的贡献
- 摘要称在所研究条件下因果结构可识别至 IN 等价模式，并以模拟和真实数据展示算法效率与效果；该等价模式的定义未提供。
- 阅读关注点
- 应核对弱自遮蔽的限制、加性噪声模型的假设、方向识别条件及 IN 等价模式的含义，并检查算法如何处理缺失机制。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实。摘要只说明特定缺失结构下的识别，不能外推至任意非随机缺失或完整因果图识别。
原文摘要
Missing data are an unavoidable complication frequently encountered in many causal discovery tasks. When a missing process depends on the missing values themselves (known as self-masking missingness), the recovery of the joint distribution becomes unattainable, and detecting the presence of such self-masking missingness remains a perplexing challenge. Consequently, due to the inability to reconstruct the original distribution and to discern the underlying missingness mechanism, simply applying existing causal discovery methods would lead to wrong conclusions. In this work, we found that the recent advances additive noise model has the potential for learning causal structure under the existence of the self-masking missingness. With this observation, we aim to investigate the identification problem of learning causal structure from missing data under an additive noise model with different missingness mechanisms, where the `no self-masking missingness' assumption can be eliminated appropriately. Specifically, we first elegantly extend the scope of identifiability of causal skeleton to the case with weak self-masking missingness (i.e., no other variable could be the cause of self-masking indicators except itself). We further provide the sufficient and necessary identification conditions of the causal direction under additive noise model and show that the causal structure can be identified up to an IN-equivalent pattern. We finally propose a practical algorithm based on the above theoretical results on learning the causal skeleton and causal direction. Extensive experiments on synthetic and real data demonstrate the efficiency and effectiveness of the proposed algorithms.